Thoughts on quantitative value investing

I want to start off by saying that the goal of this post is to get a discussion going. The topic is something I've been thinking about for a while and I wanted to formulate my thoughts, but they're not set in stone.

There has been a lot of talk recently about quantitative value investing, for some reason in the last year this concept has gained a lot of traction. A former deep value blogger Toby Carlisle has even written a book about the concept. I haven't read the book, but his presentation at UC Davis is very convincing. Joel Greenblatt has been pounding the pavement for the past few years first promoting magic formula investing, then formula investing, then his new mutual funds based on these concepts. Along with this is the proliferation of value weighted index funds.

It seems all of these products are trying to capture the value investing returns that appear in academic back tests. It's no surprise to readers of this blog that value based strategies work, and work over long periods of time. Graham's own net-net strategy has been working since first published in the 1930s, other low value measures work as well, low P/E, low P/B, and low almost anything. The fact that buying a number of low metric stocks will outperform the market over the long term is a well established academic fact.

My thought is, what do we do with this knowledge? If beating the market is as simple as running a program to buy all the low P/E stocks wouldn't everyone be rich? Why hasn't some 17 year old in Utica written a program that buys low P/E, or P/B stocks systematically without human input and programmed himself to riches? It's not because these ideas are undiscovered, most of Wall Street knows cheap anything out performs. If beating the market is as simple as programming a computer why hasn't anyone done that yet? Any why are there people working 80 hour weeks in finance when they could be sitting at home drinking scotch and letting the computer do the work?

Benjamim Graham often gets classified as a quant, modern investors boil his 800 pg Security Analysis down into a few terse statements such as "buy all the net-nets" or "buy low P/E stocks". The implication is that he was conducting some rudimentary quantitative investing back in the 1940s and 1950s. I've read Security Analysis a number of times, and I've come away with a distinctly different impression. When people mention that net-net investing only works when buying all available net-nets I think of the following quote: (emphasis mine)

"There is scarcely any doubt that common stocks selling well below liquidating value represent on the whole a class of undervalued securities. They have declined in price more severely than the actual conditions justify. This must mean that on the whole these stocks afford profitable opportunities for purchase. Nevertheless, the securities analyst should exercise as much discrimination as possible in the choice of issues falling within this category. He will lean toward those for which he sees a fairly imminent prospect of some one of the favorable developments listed above. Or else he will be partial to such as reveal other attractive statistical features besides their liquid-asset protection, e.g., satisfactory current earnings and dividends or a high average earning power in the past. The analyst will avoid issues that have been losing their current assets at a rapid rate and show no definite signs of ceasing to do so. " Graham. Security Analysis 6th ed. p 569.

That quote to me doesn't sound like Graham is advocating buying all net-nets, he's actually very clear that his recommended course of action is to only buy net-nets that either have high past earnings, are currently profitable and paying a dividend, or have a high past record of earnings.

I recognize that Security Analysis is dense, but it's well worth the read. Each time I re-read it I come away with the impression that Graham was an absolute genius. Rather than the stereotype of a statistical investor Graham advocates evaluating business prospects of potential investments, and even offers advice on how to buy franchise companies in the 1962 edition.

So is value investing really as easy as setting up a computer program and then counting the money? There are a few pitfalls that investors could identify as potentially the reasons that these quant methods exist. The first is that many of the stocks that turn up on a net-net or cheap screen are simply too small or too illiquid for anyone to invest in. I'm an investor in many of these stocks, and I'm guessing many readers are as well. I have no idea who reads this blog, so maybe a few billionaires do, but my guess is most readers are either individuals or work at smaller funds. Having a small capital base is an asset, not a disadvantage. Use this to your advantage by buying small stocks at ridiculous valuations that large funds could never purchase. There is a reason most funds don't beat the market, they're all investing in the same 500 stocks or so, and due to the small pool those funds become the market.

A second reason I've seen quoted in books is that most of these stocks are hard to purchase on a behavioral basis. Cheap stocks always have problems, they're never on the Forbes Best 2013 Stocks list. Yet this argument breaks down when considering that a computer has no emotion. If these strategies work and a computer could execute them who cares what's in the portfolio? Why is there any human bias taint? Let the computer run day and night buying and selling as some formula dictates.

I really don't think it's all that easy. The computer buying net-nets would have purchased all of the China frauds back in 2010 and 2011. The computer buying all of the Magic Formula stocks would have had a different performance from the one Greenblatt advertises because so far no one has been able to reverse engineer his formula. Of course that's the problem, what formula do you program the computer with? The formula is subject to human tinkering, and at some point enough tinkering creates a human driven computer executed fund. What is the P/E cutoff? Is it 10x and below? Is 8x cheap enough? What about cyclical companies, do those get excluded?

I discovered the Magic Formula back in 2007, the book was a solid read and convincing, but I'm glad I never pulled the trigger. I found a Yahoo! Group based on the investing technique, and after reading through all the messages I came to find out that the real life performance diverged greatly from the book. I still skim the messages in the group every few months, and there are plenty of people who followed the formula exactly and have trailed the market by a lot. Greenblatt's own funds have trailed the market since they were launched in 2009, arguably the best time to invest in distressed assets.

Concluding this post is difficult because I don't really have a conclusion, I'm unsettled. On paper investing on a quantitative basis seems great. The returns are easy, require nothing more than a computer and some rudimentary programming skills. Yet the fact that the best value investors aren't computer programmers but are deeply inquisitive and thinking types convinces me there's more to the story. Many great value investments come from human judgement, being able to identify the intangible. A net-net that's always lost money but hired a new CEO who's known to sell companies could be a great potential investment, but how do you program that formula into a computer?

36 comments:

I think investing on only a quantitative basis is reckless. The major pitfall for value investors are value traps. They need to be avoided, and it is a qualitative analysis that allows one to avoid them. Are they temporarily or permanently impaired? That is the question one must ask. And for it to be permanently impaired, it is usually due to the industry, competitors, bad business model in light of changing times, etc. Tough to quantify.

I think for value investing, the most important part should be the buy price. I put that above valuation, although obviously that is hugely important. If you are disciplined enough on the buy price, you have much more room to be wrong on the valuation. And always sell well before it hits your target, especially in this environment. I think that is one of the major reasons value investors have been struggling the last few years.

Also, see Schloss Golden Rule #10. Probably the most important and overlooked piece of advice I know of in the value investing space. And that's probably because it is somewhat of a technical criteria.

Good comment, I actually look at a past chart before buying a stock for the same reason Schloss did. I want to know if my expectations are realistic. If a stock has traded at a price I think is realistic in the past it's more likely it will trade up like that again. If my IV estimate for a stock is a value that it's never obtained in the past I need to consider why I think it'll ever be that high, and look at what I'm missing.

the success of Jim Simmons Renaissance Tech is pretty good proof that quant techniques can and do work, but is also proof of how much work and talent is required to execute those strategies. Any quant system, once introduced into the market, will eventually change the market and the expected value of the algorithms. These need to be adjusted continuously to account for constantly changing data feeds.Another thing to consider is the past success and then relative lagging, then new success of funds the Bridgeway. By definition quant funds are mimicking something other than the popular index, so its difficult to gauge how well they might actually be doing, specifically funds which lack a peer reference group.At the end of the day I see this more as a philosophical divergence. If you think the market is dynamic, you want to own growing franchises with moats (preferably at a reasonable price). If you think the market is a zero sum game, then an adaptive quant fund should be dead winner

As far as I know Renaissance works on time frames (seconds to hours, perhaps days on occasion) where fundamentals play little role and statistical methods have a big advantage. Does not tell us anything useful when it comes to investing on timeframes where the real world has plenty of time to interfere with prices.

No question in my mind that Ben Graham was primarily a quantitative value investor. It was his one and only insight: good things happen to cheap stocks when bought by the handful. Even the quote that you've bolded above confirms this impression: Net-nets + positive earnings; a computer could do that, no insight and no actual knowledge of the company's economics is required. (He engaged in some other kinds of investing, of course, control investments, arbitrage, etc -- these are what juiced Graham-Newton's returns but are not what he is celebrated for).

I read recently that he always had the feeling that he had been lucky rather than skillful, and I think this explains why.

Buffett's break from the Graham school is one that he downplays out of affection and respect, but it is the most significant event in value investing: it is the point at which the underlying business (rather than the stock or the printed financial statements) came into view.

He moved away with some of the frankly ludicrous aspects of the Graham approach -- the prima facie primacy of the current account, for example, and the margin of safety as a % haircut, and, for the first time, considered what the business actually worth. Buffett, it should be nored, never felt lucky, and when he screwed up (original Berkshire, the Baltimore department store, etc, it was when he bough Graham stocks singly rather than in bunches.

All quantitative methods seem to have worked: not just so-called "value" approaches to quant value but also ones based of price momentum, hybrids like the F-score, etc. Some Q investing approaches have performed much better than Graham's statistical approaches, whether the net-net, Ncav or 6x6x6. The choice between is determined by an investor's ability to constuct a pleasing narrative about this or that statistical method and why it has worked in the past; it allows them to believe that it will continue to work in the future.

Others, like me, cannot construct a nice, plausible narrative about any of these statistical methods and so we don't engage in it. And, if there are very many people like me, that may be why these methods -- 2/3 NCAV, RSI above 50, etc -- actually work

My main point, probably somehwat lost in my comment above, is that people care about the narrative behind the method more than they do about its results. To some, "this stock is selling below cash" is a compelling narrative, to thers, it isn't. To some, "above average business" at below average price" is a compelling story, to others it isn't. To some, "wonderful business at fair price" is a compelling narrative, and to others it seems like folly.

Investing is, to private investors, a hobby. To some people, the fun in it is spotting turnarounds, to others it is catching a falling knife, to others still, it is being ccntrarian, taking the other side of Whitney Tilson's trades, identifying the next ten bagger, riding momentum, scouring the footnoted, playing out chapter 11 scenarios, calculating merger closing odds, coding and back-testing a "magic formula" of some sort,tracking Phase 2 trials, anticipating next years proved reserves, whatever. Not principally as a means to make money, although that's aurely part of it, but as a pastime. (The number of private investors "who have a strongly held macro view, for exampe, is something of a clue to this). Different kinds of activities deliver different highs, depending on who you are.

Professional investors, of course, differentiate themselves for marketing purposes. To them AUM is far more important than returns, and returns have only to be good enough to allow AUM to grow (or stay large). Differentiation, by definition, means a dispersion in approaches from that of Richard Pzena to that of Cliff Asness. Both, I'm sure, would call themselves "value investors".

You raise a good point which is that different actors have much different motivators. The private investor is looking for a safe positive return, whereas the professional is looking for at least average returns or better. The professional has career risk which limits their actions.

The problem with quant strategies is that in the long-term they are zero-sum as the strategy fundamentally changes the nature of the market. Either you get copied or you get too big. I think the only sustainable quant strategy is one that can move with changes in market structure. Having said that, some stratgies do seem to work in the long-term, for example going long/short low/high beta. So any naive value strategy seems problematic, especially when more and more money seems to be going this way. The only way to survive, in my opinion, is just to stay focused on buying cheap companies and don't deviate from a focus on absolute value. AQR's quality companies screen seems interesting and profitable but the whole concept is fundamentally troubling to me. You have found a method which describes past returns well...congratulations but it doesn't say much about what will happen in the future.

Anything you can easily program will be arbitraged out of the market very quickly, so if a value quant screen worked, people would pile onto it until it didn't work.

The other problem with quant screens is they do not recognize trend changes in the market, as you suggested when China stocks went from being darlings to untouchables.

You could write a program which was quant-oriented, but allowed for input and tweaking, but then you'd need an organization like Renaisance and really smart guys, lots of money to buy computers and outstanding management to make it work.

Yes, you hit on the essence of my problem with quant investing, it's so dynamic it's not as simple as just programming a computer. The programmer really need to be a savvy investor who's able to identify the trends and change with them. At this point what's the difference between the programmer and a fund manager making the same sorts of portfolio management decisions?

I have thought a lot about this topic and even wrote a book concerning it titled, "The Emotionally Intelligent Investor: How Self-Awareness, Empathy and Intuition Drive Performance."

The consensus opinion on Wall Street and on Main Street is that investing success comes from blocking out emotions and making purely rational decisions. While there are many emotional biases that lead to investing error, tapping into certain feelings can very often also be beneficial. Instead of blocking out all feelings, great value investors like Warren Buffett actually heavily rely upon at least two helpful emotional brain processes: intuition and empathy.

Intuition is not some sort of magical sixth sense. Instead, it is a complex feeling that arises from pattern recognition. Chess grandmasters usually know their next move within a few seconds. Instead of suppressing their emotions, they first utilize gut feelings regarding their best possible move depending on how the pieces are laid out on the board. The intuition is the result of years of study and practice. These grandmasters then use their reason to make sure the move is safe. If the initial gut instinct is found to be flawed, they start the cycle again with another intuitive feeling. There are too many possible moves for chess to be played any other way. Many of the best investors seem to do something similar. For example, Buffett does not start his investment process by comparing a bunch of possible investment alternatives. He does not heavily depend on quantitative screening tools. Instead, Buffett intuitively gravitates towards a company he finds interesting and understands. He then analyzes the company, its industry, and its valuation to determine if the investment makes sense. If it does not, he moves on to the next company his intuition leads him to analyze. If the potential investment seems safe or attractive, Buffett refers back again to his intuition regarding the management’s competency and trustworthiness. He makes a judgement as to how much upside "optionality" there is with the investment. He also utilizes gut instincts with position sizing, overall market exposure, and in sensing danger. For example, he doesn’t completely analytically decide that one investment should be $1 billion while another one should be $300 million. A significant part of that decision is based on intuition.

Buffett advises “to be fearful when others are greedy and to be greedy when others are fearful.” This statement implies a reliance on empathy. Empathy is the ability to put oneself in another person’s shoes and feel what they are going through. Buffett’s investment choices are directed towards companies and industries where he senses others’ fear. Buffett knows that every buying decision he makes is associated with someone else choosing to sell. While it is true that feelings like fear, sadness, shame and regret can lead to behavioral biases, which negatively impact decision-making, successful investing also involves utilizing empathy to take advantage of others making these common mistakes.

In summary, Buffett shows us that investment success does NOT come from blocking out emotions. Rather, it comes through having the self-awareness to know which feelings are helpful and which are hurtful to your decision-making process. It also requires the deliberate cultivation of relevant intuition. Just like a chess grandmaster, Buffett’s intuition did not develop overnight. It is the result of years of experience and study. Finally, success depends upon developing the social awareness to be able to recognize when others ARE making mistakes because of some of their emotions.

I think you've hit upon something I've been thinking about for some time. Behavioural bias is oversold as a concept, IMO. Whilst it may be true that we do have biases, most/all of this can be overcome by pattern matching. After all, when you know what to do, emotion is no longer a problem. Pattern matching can only come with experience, and it is often difficult to tease out the important factors amongst a plethora of factors that is relevant to success.

The trick is to try to develop good patterns that are highly predictive of a high upside. These patterns are likely to be interspersed with a lot of noise to the contrary - which is is of course why it's generally quite hard to beat the market.

Computer programs now account for the majority of the daily volume on most stock exchanges. If we are going to outsmart the computers and other human players in the market, we will increasingly need to harness some of our emotional thought processes more effectively. Feelings are our main competitive advantage against the machines and ever more against humans who are increasingly suppressing their intuitions.

I wrote briefly about magic formula experience here:http://fowci.wordpress.com/2013/01/07/2012-performance/

Basically, I pointed out that there are emotional issues with sticking with a formula. My use of the magic formula has dramatically underperformed the market.

I had allocated 1/2 my US portfolio to magic formula stocks. Excluding those stocks, I equaled the market last year. With those stocks, my overall portfolio was up 7.2%.

Even if the magic formula delivered outperformance in the long run, it's hard to stick to it when you have no real idea about the underlying companies. I've suffered through underperformance with other stocks, but I can just go back to my notes and make sure I'm still ok with my thinking.

With the magic formula (or perhaps any other quant stock), I have some stocks that have dropped 60%. Then I go read up on them and find they're for profit education stocks that I bought just as the hint of regulation/cutting funding arose.

I would never buy those stocks with a traditional value approach until I was sure that the market was over predicting the negative effects of regulation/cutting funding.

(There's another big issue - the MF doesn't seem to have worked for anyone over the long haul since the book was published. Even the mutual funds he runs are lagging the market).

All of that said, I've been tracking a quantitative value portfolio since June 7 last year, when I first ran a screen on turnkeyanalyst (Wes Gray's website). Since then the S&P500 is up 13.09%, while the quant portfolio is up 20.7%. I trust Wes Gray more than Joel Greenblatt (I've been reading turnkey analyst for a long time).

But next time I experiment with a quant portfolio I might restrict it to 25% of my portfolio for a few years.

"So is value investing really as easy as setting up a computer program and then counting the money?"

No, for a few reasons -

- value traps - qualitatively you might hope to screen these out to avoid potential losers (e.g. newspapers, Blockbuster) - news developments - computer programs won't be able to pick up recent news releases (positive or negative surprises)- data issues - computer programs won't pick up inconsistencies between the screening data and qualitative information you might pick up from the financial statements (do the statements/data reflect the 'real' cash coming through the business?)

So many studies have shown the statistical outperformance of the cheapest/largest quintile/decile by whichever measure you choose, but when entering your 'buy' order, we need to bear in mind that we are not buying the that quintile or decile of hundreds of stocks from 10/30/50 years of backtesting, but an individual, specific, SINGLE stock, even if you've managed to get round the issues highlighted earlier.

That said, I am a strong believer in decent screening serving as a starting point for identifying potential investments, followed by a decent dose of qualitative assessment.

Your points are a lot of the things that I've wondered about. I know just looking at different companies that often the data sources are wrong, so a quant program would be using incorrect data, obviously a problem.

The value traps and human judgement are really what are key for me. Being able to look at a financial statement and see that earnings are depressed because a company just built a new factory that will contribute significantly in the future. How does a computer know that? That particular company might fall off the screen because it failed to meet a certain profit threshold.

From the comments it seems like a consensus is forming that quant strategies work, but only when the manager is able to tinker and modify the programs endlessly.

I really like your point about not buying backtested returns, but buying an individual company, so true!

Incorrect data is okay as long as you're right more often than not, besides the set of error buys are theoretically random aka neutral returns (otherwise if you hit reliably negative alpha, flip the sign of your trades and bingo).

Value traps is not impossible to do quantitatively, you just layer a bankruptcy prediction model on top of your initial scan. Same for news, you can detect some via chart patterns or twitter processing. Of course all this is hard work so the qualitative methods can still compete I think.

Also quantitative can support qualitative, because people are generally crap at risk management. Teaming qualitative guys with quants for trade execution should do really well. I think even very primitive "quant" rules (max N% of portfolio entry, sell half every time it doubles, or the like) would enhance performance of most qualitative people. Pity (or opportunity?) the cultures rarely mix.

"incorrect data is okay so long as you're right more often than not" - have to disagree there. the key premise there is "you're right more often than not". If the premise is true then nothing else matters. Eg picking stocks blindfold is ok so long as you,re right more often than not.

Theoretically random and practically random are very different in theory and possibly in practice.

Agree that you can pick up news etc via combo of charts/twitter but not to the same qualitative extent. Charts/twitter might give you the presence and direction of news but it doesn't give you the longer term impact eg one off issue, cyclical or long term structural?

Agree that a mix of quant and qual is generally beneficial. The caveat to all of this is that it can be just as easy to get the qualitative calls wrong.

Btw, my definition of quant in e this discussion is only in relation to small scale investors putting together simple screens and the odd spreadsheet. Quant as defined by firms like the renaissances, bridge waters of this world is a different matter!

What fowci said above: "Even if the magic formula delivered outperformance in the long run, it's hard to stick to it when you have no real idea about the underlying companies. I've suffered through underperformance with other stocks, but I can just go back to my notes and make sure I'm still ok with my thinking."

And what Graham said: "Investing is most intelligent when it is most businesslike."

That said, Graham himself moved closer and closer to mechanical/quantitative approach towards early 70's. In some of his writings/interviews in that period he said security analysis no longer added any value.

I think a quant screen is a good place to start, but it has some tremendous flaws/traps.

A perfect example of this is the "for profit" educational sector. In the past two years some of these stocks are down well over 90%. This sector had traditionally been a "high flyer". The sector enjoyed tremendous growth, and incredible margins. As such, the companies in the sector had seemingly good balance sheets, cash flow and earnings.

HOWEVER, the entire sector is based off of predatory student loans underwritten primarily by the federal government. There is a very strong chance that government reform will come about. Any reform is going to be TREMENDOUSLY damaging to this sector. I would not be surprised if most of the players are out of business in a few years time.

Most of the time that these stocks were going down, they were appearing on the "magic formula" screens and have been "value traps".

I guess my point is this...with a couple days research & due diligence it would have been apparent that the entire sector was in danger of total collapse. A purely quantitative screen would not have seen this and in fact would have been fooled, resulting in tremendous losses for the investor...

As has been mentioned repeatedly above, many stocks which look like good deals on paper face challenges which are qualitatively obvious. On the average that means that purely quantitative valuations will have a negative correlation with qualitative factors. Put simply: the better the numbers look, the more likely there are to be mitigating factors outside of the numbers.

I think I can understand the argument that one might want to keep a quantitative method "unbiased" by human interaction. Such methods would be refined with historical data, for which qualitative assessments are presumably unknown. Unable to assess the impact of qualitative impressions on the method, you justify their omission in practice. After all, you didn't need them to generate your back-testing results. Unfortunately, just because you ignore qualitative factors doesn't mean they'll ignore you. The simplest example of this is an end-of-an-epoch event that obviates the historical results your model was refined on. Clearly qualitative, clearly not included in your model, but worthy of consideration nonetheless.

Personally, I want as much information as possible when making investments. I therefore form quantitative and qualitative assessments of a company, and then make predictions and assess the uncertainty of each.

A lot of problems with quantitative investing have been mentioned above, and in my opinion they are all true: a human might spot a clear bargain which is not readily apparent from the financials and a quantitative screen might select a lot of value traps.

You could start with the results of a quantitative screen and try to improve on that by manually filtering out the value traps, but will you not filter out some of the biggers winners at the same time? Let me give some examples of what a human might do:

*Had you looked at the Magic Formula screen at the start of January, you would have found Herbalife (HLF). You would probably have known that Bill Ackman has a big short position in this stock and might have decided to not invest here. Had you made this decision, you would have missed out on 35% gains so far. Of course the verdict is not out and the stock may still go to zero, but I’m just saying.

*Some quantitative screens currently select Orient Paper (ONP). This stock looks ridiculously cheap with a P/E of about 1.8 at the beginning of January. But this stock is based in China which means a high risk of a fraud. Just ask investors who were invested in the Magic Formula in 2010. I believe there also currently allegations of fraud against this company. You might decide not to buy this company, but then you would have missed out on 45% gain so far.

*The Magic Formula is almost continually ‘plagued’ by biotechnology companies. I believe these companies get a large lump-sum from their sponsor when they reach a certain milestone. This leads to enormous trailing earnings yields and returns on capital, but a human will immediately recognize that this is an anomaly and that coming up with normalized numbers seems almost impossible, and might not select these kind of companies. But then he might also miss out on some gains. For instance, had one bought an equal weighted portfolio of all the biotech stocks in the Magic Formula in June of 2010 which had been unprofitable up to the previous year (BDSI, ZLCS, CYTK, IMMU, SNTS, SNTA) and held these until October of 2012, the portfolio would be up 77.6% (27.9% annualized). These gains come from multiple stock performing very well: the returns for the individual stocks over the period mentioned are respectively 145%, -62%, -72%, 6%, 272%, 176%. I have no more data than this, so this is by no means a conclusion that one should not throw out biotech firms. I’m just saying that if you filter out anomalies, you might also miss out on big gains.

Joel Greenblatt has said that he was not able to improve the results of the Magic Formula portfolio by manually selecting stocks from the results. I don’t know if he’s telling the truth, but at least I don’t doubt his investing skills, so if he is telling the truth, that says a lot of how difficult it is.

You might work on improving the screen instead of trying to manually edit its results. For instance you might not look at last year’s earnings, but at the average of the last 2 years of earnings. I’m sure Joel Greenblatt has made improvements over the public version of the Magic Formula when he screens for his own firm, because I saw a video of him talking about the screen to his Columbia University class where he mentions this (he just doesn’t say which improvements he made).

"You could start with the results of a quantitative screen and try to improve on that by manually filtering out the value traps, but will you not filter out some of the biggers winners at the same time?"

YES. You are exactly right.

Someone once dubbed quantitative value screens as "a glass ceiling, not a base from which to build from". Damodaran produced some statistics that showed actively managed value funds underperformed value indices. Large cap value showed the greatest discrepancy.

The big problem seems to be is that although active investors may be able to avoid some of the worst dogs, they tend to overtweak their selection process, thereby producing a net negative.

Regarding the issue that if quantitative strategies really work, they will automatically arbitrage themselves away, I disagree. Sure, the mechanics can be easily automated, but it is still a human who makes the decision to allocate a certain amount of money to a strategy, keep it there for the long run and not tinker with the strategy. Emotionally speaking, that is not easy at all.

Every (value) investor needs patience, discipline, courage, an ability to stand apart from the crowd and think in the long-term. Most investors don’t have this. Besides that, a quantitative investor also needs to believe that a strategy that worked in the past will keep working in the future, even though there may be new developments (think Chinese reverse take-overs for example) and even though the problems with these strategies are obvious.

It’s very difficult to invest your hard-earned money in companies of which you don’t have any idea what their intrinsic value is or where you think there’s a good chance that their intrinsic value is zero (think Chinese companies, Herbalife before Dan Loeb went public with his long position, biotech companies, etc.).

The fact that a computer does the investing for you makes it easier, but not easy. Most investors are still inclined to look at their portfolio, and won’t be pleased to find there all the companies which have been negatively mentioned in the news lately, especially if the portfolio has underperformed for the last 2 or 3 years.

The problem (and at the same time blessing in disguise) of these strategies, is they can underperform for multiple consecutive years. By that time 99% of the investors will have abandoned it, thereby ensuring that the strategies will keep working in the long run.

Your remark that the best value investors are deeply inquisitive thinkers and not computer programmers, fits with my experience. If you are confident in the intrinsic value and margin of safety of your portfolio, you are better able to withstand long periods of underperformance and volatility because you believe that eventually the portfolio will go toward intrinsic value. If you do quantitative investing, you can only rely on your belief that strategies which worked in the past, will continue to work in the future, to guide you through large volatility and long periods of underperformance. This is much more difficult in my experience.

In my experience, quantitative investing is at the same time much easier and much harder than Ben Graham/Warren Buffett-style value investing. It’s much easier because it requires no knowledge, experience, skills and time to be successful. It’s much harder because you have no idea of the margin of safety of your portfolio and continually have to fight the urge to tinker with the screen or manually change the results.

So regarding to the question, “If it’s that easy, why doesn’t everybody do it and why isn’t everybody rich?” my answer is, “It’s not that easy.” It’s just that the difficulty isn't in the mechanics, but in the emotional intelligence required.

Quantifying one's value investing approaches into an algorithm (provided you have sourced the appropriate data, in the appropriate format and it's accurate and continually updated/adjusted and accurate...which is no given and not easy to do manually) is actually a straight forward process (you could do it in VBA, C, Python, etc.). In my view, the process is just a more tailored and specific approach than one would do in creating a valuation based screen on Bloomberg’s screening tool.

My issue (and it's the issue with all investing approaches) is first one has to get the value model correct and then the second thing is study the output of the model against enough historical data to understand statistically to a measured certainty level whether the model is outputting useful results or not. If you're looking for un-hedged long stock positions the model needs to consistently beat the S&P 500 over time (given a statistical certainty level), otherwise you can't turn things over 100% to the machines.

One simply would end up using the quantitative value model as a "tool" to help identify candidate stocks. Much further research and analysis would be needed before one lets the "machines" make the buy and sell investing decisions.

This is where the tough part comes in. The future vs the past. I only trust my value screens so much. I combine the valuation screen with some standard technical analysis to help with "timing". The value screen combined with my technical analysis metrics output a basket of stocks for consideration.Then the real work starts in going through the financial statements and notes, the press releases, identify the industry trends and profit outlook, competition, changing technologies etc.. All the time consuming tedious work that investment research requires if you’re going to get an information edge. Some people say this information is already reflected in the stock price, but in many cases this may not be true. As we've seen in recent years with drastically good or bad implications depending on whether you were on the long or short side of the trade (banks, mortgage companies, Enron, etc.).

Every company I consider for a long or short position, even if it gets by the valuation screens is modeled into the future. The base forecast model (depends on the type of company) is 10 years. It's the automation of the future valuation model to help with the forecasting that is really tough (my models are not simplistic 10 line models like many analysts do these days). Automating a forward looking valuation model is much tougher than doing a backward looking algo to get the basket of stocks. I do at least three scenarios of the forecast valuation model. Base case, worst case and best case.

(con't)In going through the forecast valuation/modeling process, if I can't clearly develop/identify the main reason for the "driver" of the increased value (and price) of the stock, then I have no interest in taking a position. For a margin of safety, I need about 20% upside valuation on the base case relative to the current market price for a long and generally the same 20% to the downside for a short position (this also helps with timing).

Often I'll model a company, and the upside and everything else looks good. But in going through all the "soft" intangibles of the company this is something that I just don’t like. For example, I just might not like the corporate ownership structure (a big problem with many companies, especially emerging market companies in some countries). Thus, I won't pull the trigger or I will add an extra point or two to the discount rate to adjust for this risk(it's tough when that happens, but I've seen what can happen to minority shareholders when dealing with some of these majority owners).

One can "code" a "risk intangible or set of risk intangibles" into the value screen algo to compensate for the fact that certain countries rank higher than others for corporate governance, but really it's a risk assessment that needs to be scored on a company by company basis. This requires going through every company in the database and giving it a rank score for the ownership structure and corporate governance. No easy way around that type of research work.

An example: I valued Cisco back around 2004. I determined that after the tech crash in 2000 through to 2002, that Cisco would continue to be a dominant company in their core router business, but I also foresaw that the declining trend in unit bandwidth prices paid by end users would continue to come down for a long time with the carriers (Cisco's customers). Meaning that the carriers (Cisco's customers) would continue to demand better/faster core routers. But that the carriers would not be willing to pay more for the better router throughput (unit capacity basis) as their revenue was probably not going to increase much (days of 10% revenue growth for the carriers were over) unless the increase in traffic significantly outpaced the unit price decline the carriers were experiencing from their customers. At the time, the unit price declines were actually outpacing the increase in traffic. I don't know if that's the case anymore, but you get the picture. I did not see the huge revenue driver for Cisco. They would continually be asked to create faster and faster routers or the carriers would threaten to go with another vendor who did have the faster routers. At the time I remember (back in 2004) that I valued Cisco around $19 or $20 per share. Today, Cisco trades at $21.15. It's gone nowhere in more than 10 years. This is not a growth company. But most technology based companies don't think of themselves as everyday basic companies that should focus on free cash flow within the business cycle of stable market and pay out their excess cash in dividends. In the pursuit of “growth” they create their own investment divisions and overpay for start-up firms attempting to buy growth at a level that they command a "growth multiple". Many of the investments don’t work out and the company ends up hurting their return on assets and return on equity through the bad investments and keeping too much excess around (many companies simply go into decline in their core business, such as Nortel). If I remember correctly, Cisco was forced to start paying a dividend when shareholders started complaining (it's about 2.7% yield now according to Yahoo! Finance)

(con't)In summary, how does one "code" the future outlook story such as I just told above into an algorithm? It’s very difficult to do implement this type of forward looking research logic in an automated algorithm. One might be able to do it after they have done all the research work and valuation model work and given Cisco a "future outlook" score. But you can see that the research analysts would play a vital part in the development and maintenance of the automated value algorithm code.

I may have even been wrong in my future assessment at the time and Cisco has been flat for many years for completely different reasons than I described. But in the end I got the valuation right. There was no future valuation driver to push the price higher. My valuation assessment was overall correct, whether or not the specific reason driving the story was right or not.

No quantitative value screen is going to be able to foresee the future for an individual company (and we know many analysts get it wrong). Maybe one could implement quant value screens with success at the aggregate level for industries, sectors and the entire market. But then, you just need to run the quant value screen on a bunch of sector or index ETF's and make life easier.

1) Quantitative strategies really only work well in inefficient markets, such as small-caps, or during market panics, where mis-valuations are much more likely to be large and obvious.

2) It is emotionally very difficult to stick to a quant system during periods where it lags -- you are buying stocks you don't understand for reasons you don't understand. You will have no confidence in the method and are likely to modify it or to bounce from method to method.

3) If on the other hand you are skilled enough to understand or create a decent quant method, then you are probably skilled enough to use all kinds of other data in your analysis of companies -- industry factors, macro judgements, trends in historical data, ideas about management, judgements about disruptive factors, and so on.

Thus it seems to me that the only people who are truly capable of using a quant strategy where it works (small caps) are exactly the same people who would see it as an incomplete approach and at the very least over-ride its buy signals with some frequency.

This is what happened to me. I started with a quant system I built. I know the system works because I now have years of out-of-sample data on all the stocks I've scored while searching for investments. But I no longer even pretend that the system guides my investments. I still score stocks, but I view my quant system as just a useful checklist of criteria.

It is funny how in a discussion of quantitative strategies many people have opinions and rationalizations, but few have tangible evidence (data, especially with robust tests) to back up their opinions. That's why simple statistical models outperform human intuition. Greenblatt (even though his data or test appear to be flawed) provides an explanation of the institutional factors that make the value approach sustainable. Gray and Carlisle address the issue in more length and address the elements of human nature that contribute to the sustainability of a quantitative approach.

Is the intent not to understand how things really work (to drive up one's own returns) rather than rationalize why it is okay to continue doing what was being done?

I'm reading Quantitative Value right now actually, and it isn't as simple as some make it out to be. For example there is a LOT of work finding the "right" formula, the one that will out perform. I have heard Greenblatt spent $20m massaging his data for the Formula Investing. So how do we know we picked the right formula? The one that back-tested the best?

I think the quantitative stuff can be a great guide, but I don't think it's a panacea. Yes human emotions drive the market, but successful investing isn't as simple as programming in a formula and sitting back and letting the money roll in. The difficulty is picking the right formula, make a slight error, or pick some of the wrong variables and the formula approach could end up doing worse than the market, there's still a human element involved.